Current Radiology workforce perspective on the integration of artificial intelligence in clinical practice: A systematic review

Abstract

Introduction

Artificial Intelligence (AI) represents the application of computer systems to tasks traditionally performed by humans. The medical imaging profession has experienced a transformative shift through the integration of AI. While there have been several independent primary studies describing various aspects of AI, the current review employs a systematic approach towards describing the perspectives of radiologists and radiographers about the integration of AI in clinical practice. This review provides a holistic view from a professional standpoint towards understanding how the broad spectrum of AI tools are perceived as a unit in medical imaging practice.

Methods

The study utilised a systematic review approach to collect data from quantitative, qualitative, and mixed-methods studies. Inclusion criteria encompassed articles concentrating on the viewpoints of either radiographers or radiologists regarding the incorporation of AI in medical imaging practice. A stepwise approach was employed in the systematic search across various databases. The included studies underwent quality assessment using the Quality Assessment Tool for Studies with Diverse Designs (QATSSD) checklist. A parallel-result convergent synthesis approach was employed to independently synthesise qualitative and quantitative evidence and to integrate the findings during the discussion phase.

Results

Forty-one articles were included, all of which employed a cross-sectional study design. The main findings were themed around considerations and perspectives relating to AI education, impact on image quality and radiation dose, ethical and medico-legal implications for the use of AI, patient considerations and their perceived significance of AI for their care, and factors that influence development, implementation and job security. Despite varying emphasis, these themes collectively provide a global perspective on AI in medical imaging practice.

Conclusion

While expertise levels are varied and different, both radiographers and radiologists were generally optimistic about incorporation of AI in medical imaging practice. However, low levels of AI education and knowledge remain a critical barrier. Furthermore, equipment errors, cost, data security and operational difficulties, ethical constraints, job displacement concerns and insufficient implementation efforts are integration challenges that should merit the attention of stakeholders.